Optimizing Energy Consumption

Overview

We look at parallel algorithms that adapt as power reductions
constrain the speed or number of cores that can be turned on,
or as the number of entities available to work together changes.
How can they be made to use less energy for computation and
for heat removal?

In a smart grid, the grid operator can sense what appliances are in use and possibly turn them off. How can we ensure privacy and security of customer interest? How can we ensure stability of this large dynamical system, while accommodating variable green energy sources like solar and wind? Can we design auctions to encourage off-peak use and guarantee stability?

Another aspect is making algorithms themselves more energy efficient. For example, we have developed power-aware parallel algorithms that change their behavior as the available power changes. In supercomputers there may be hotspots, forcing some regions of the system to slow down relative to the other regions even though they are all working together. How can our algorithms dynamically adjust to this?